It is generally recognized that the installation and use of an energy storage system (“ESS”) on an electrical grid can result in material benefits (operational, financial, environmental, etc.) to grid participants and/or stakeholders, and by doing so can generate material financial returns to an entity owning or controlling the energy storage assets. Energy storage techniques can generate these kinds of benefits through a range of potential energy storage applications (“ES applications”), such as (i) the provision of certain ancillary services for which there are established energy or capacity market mechanisms (e.g., frequency regulation, spinning reserves, black start capacity), (ii) load shifting or peak shaving, (iii) deferral or avoidance of otherwise necessary transmission or distribution upgrades, (iv) relief of transmission or distribution bottlenecks or other constraints, (v) integration of intermittent renewable generation, whether through smoothing, ramping services, the provision of shaped power or otherwise, (vi) hybridization of generation assets to increase fuel efficiency or reduce carbon emissions, (vii) provision of backup power or uninterruptable power system (“UPS”) during islanded operation, (viii) time shifting of energy purchases and sales for cost saving or arbitrage purposes, (ix) provision (or committed availability to provide) of various operating reserves, (x) provision of power, energy or services that might otherwise be provided by a natural gas peaking plant or other power generation sources, to name a few. The foregoing is intended to be a representative listing of ES applications, and not an exhaustive listing. In many cases a single ESS installed in a specific location can provide multiple ES applications (sometimes referred to as the stacking of applications). As used herein, references to a single ES application may include a combination or stacking of multiple ES applications.
The existence and extent of the benefits and/or related financial returns from a specific installation and use of an ESS can be dependent on a broad range of factors. These factors include the cost of the ESS (which is generally measured in terms of $/kW and/or $/kWh), the ESS's ratio of power to energy, the size of the ESS (in kW or kWh), the round trip efficiency of the ESS, the cycle life and/or useful life of the ESS, the manner in which acquisition of the ESS is financed, the site and installation costs of the ESS, the ongoing operating and maintenance costs of the ESS. Additional factors can also relate to the location of the ESS installation and the ES application(s) for which it is used. These factors can include energy prices and other market conditions, the specific grid conditions giving rise to a need for the ES application, the pricing/compensation/tariffs or other incentives available for the product or service provided by the ES application, the reliability of forecasts of available power, and the mix of generation assets serving the geographic (or the collection of electrical connections to an ESS) area that includes the ESS.
There currently exist a number of sophisticated models that can be used to control an ESS. These models are however limited in that they only reflect abstract representations of a possible reality and assume perfect foresight. In practice, operational conditions of a typical ESS in the field are most often dynamic and different from these abstract models and their assumptions and the forecasts based on these models can only be made within a limited degree of certainty and accuracy. Hence, needs exist for systems and methods that provide technical solutions in terms of modeling at a higher degree of certainty and accuracy. In particular, in the fields of ESS operational control, needs exist for technical solutions that are capable of achieving one or more, or any possible combination of, at least the following technical objectives: (1) optimal performance in ongoing future prediction of ESS parameters, such as electrical quantities, that affect ESS configuration and operation; (2) quantification of forecast uncertainty and provision for adequate remediation strategies; and (3) scheduling and dispatching an ESS adaptively to track dynamic and evolving ESS parameters, such as electrical parameters.